TY - JOUR

T1 - Stack bricolage and infrastructural impermanence in financial machine-learning modelling

AU - Hansen, Kristian Bondo

AU - Thylstrup, Nanna

N1 - Publisher Copyright: © 2023 Informa UK Limited, trading as Taylor & Francis Group.

PY - 2023

Y1 - 2023

N2 - Hoping that the promises of machine-learning can be realised in financial markets, investment management and trading firms increasingly employ machine-learning techniques to extract exploitable informational edge from large datasets. In addition to heavy investments in technology and the human resources capable of manipulating it, this development has led to increased use of open-source machine-learning and data-management resources. Drawing on 44 interviews with developers and users of machine-learning techniques in the finance, we explore how such platforms and other open-source resources are understood and used by said practitioners. Building on work in the Social Studies of Finance (SSF) on financial modelling and platformisation, we argue that these users of machine learning in finance engage in what we term stack bricolage activities, when they reuse disparate open-source resources in their modelling work. We argue that stack bricolage creates dependencies on open-source cloud resources characterised by infrastructural impermanence, which is a result of their substitutability and maintenance sensitivity. Our study contributes to the emerging SSF literature on machine-learning modelling cultures and debates in Science and Technology Studies and adjacent fields on the reuse of data and software in platformised cloud infrastructures.

AB - Hoping that the promises of machine-learning can be realised in financial markets, investment management and trading firms increasingly employ machine-learning techniques to extract exploitable informational edge from large datasets. In addition to heavy investments in technology and the human resources capable of manipulating it, this development has led to increased use of open-source machine-learning and data-management resources. Drawing on 44 interviews with developers and users of machine-learning techniques in the finance, we explore how such platforms and other open-source resources are understood and used by said practitioners. Building on work in the Social Studies of Finance (SSF) on financial modelling and platformisation, we argue that these users of machine learning in finance engage in what we term stack bricolage activities, when they reuse disparate open-source resources in their modelling work. We argue that stack bricolage creates dependencies on open-source cloud resources characterised by infrastructural impermanence, which is a result of their substitutability and maintenance sensitivity. Our study contributes to the emerging SSF literature on machine-learning modelling cultures and debates in Science and Technology Studies and adjacent fields on the reuse of data and software in platformised cloud infrastructures.

KW - Financial markets

KW - infrastructural impermanence

KW - machine-learning

KW - platforms

KW - reuse

KW - stack bricolage

UR - http://www.scopus.com/inward/record.url?scp=85168692330&partnerID=8YFLogxK

U2 - 10.1080/17530350.2023.2229347

DO - 10.1080/17530350.2023.2229347

M3 - Journal article

AN - SCOPUS:85168692330

JO - Journal of Cultural Economy

JF - Journal of Cultural Economy

SN - 1753-0350

ER -